A multifunction peripheral having multiple functions in one structural unit, for example the functions of a printer and a scanner, is employed to achieve various purposes. During operations, an original document is scanned by the scanning module of the multifunction peripheral, and the pixel data of the scanned document are stored in a buffer. The pixel data are then outputted from the buffer to some image processing modules. After a series of image processing operations are performed on the pixel data by the image processing modules, the processed image data are stored into the printer buffer and then printed. Examples of the image processing modules include color space conversion (CSC) modules, color photo/text separation (color PTS) modules, color background removal (color BGR) modules, color filtering modules, color management modules (CMMs) and halftoning processing modules.
In these image processing operations, the photo/text separation is very important because the processing result of the photo and text separation may influence the subsequent color background removal operation, the color filtering operation, the color management and the halftoning processing operation.
In order to achieve excellent photo and text separation performance, the researchers are focused on finding out proper photo and text separation algorithm and parameters. There are several photo and text separation algorithmic methods. The parameters used in each photo and text separation algorithm are diverse. For achieving optimized photo and ext separation performance, the researchers should try many photo and text separation algorithmic methods while changing different parameters. Unfortunately, since no suitable tools are provided for evaluating the photo and text separation result, the photo and text separation performance fails to be largely enhanced. In other words, the photo and text separation result should be manually evaluated. After the photo and text separation result is printed on a paper, the erroneous photo and text separation blocks are marked with the naked eyes. For example, a photo block which is identified as a text block is referred as an erroneous photo and text separation block.
Since the blocks of each image to be examined are abundant and very tiny, the visual identification is time-consuming and always results in fatigue of the examiners' eyes. In addition, since no objective tools are provided, different photo and text separation results are obtained from different examiners. Accordingly, it is important to develop a criterion for determining the photo and text separation result by calculating the photo and text separation performance.